In any industrial process, it is necessary to monitor process data channels in order to detect any changes that might affect the final product. As the complexity of the process increases, the number of data channels that must be monitored also increases. In extremely complex processes, hundreds of data channels must be analyzed to determine the state of the process.
Although sometimes a matrix notation is used, in general a process state could be described as a function:Process State=f(a1M1,a2M2,a3M3, . . . , anMn)where each variable Mn represents a specific process data channel and each an represents a scaling coefficient. The scaling coefficients are selected to modify process data channels in such a way as to optimize the results of the function for detection of process changes.
Evaluating a function (or functions) whose variables are process data channels is one method for analyzing multiple data channels and producing an output that describes the process state. This output can then be used for process change detection. FIG. 1 represents a generic process change detection system described in terms of a process state function. Data is collected by at least one detector and sent to an analysis unit. In the analysis unit, the data channels are combined with the scaling factors and entered into the process state function which is then evaluated. The output of this function is then available for the detection of process changes.
An illustrative example of process change detection can be found in the semiconductor industry. One of the steps employed in manufacturing semiconductor devices is plasma etch. In plasma etch processes, a sample is exposed to a plasma designed to etch away specific materials from the sample surface. Often it is necessary to stop the plasma etch at a specific time to achieve a precise etch depth. The time at which the plasma etch should be stopped is called the process endpoint. Determination of process endpoint is often done using a multi-channel technique called optical emission spectroscopy (OES). FIG. 2 describes a typical OES endpoint detection system.
During a plasma etch, the plasma emits electromagnetic energy in a wide range of wavelengths. The exact spectrum emitted by the plasma is dependent in part on the presence of volatile byproducts created during the etching of the sample. In a typical OES endpoint system, a spectrometer is used to separate the plasma emission into discrete wavelengths. The intensity of the emission at each wavelength is measured and becomes a separate process data channel which can be monitored over the course of the etch. By monitoring those data channels that show a repeatable variation during the etch, it is possible to determine when the sample has been completely etched.
In its simplest implementation, an OES system can be used to monitor a single data channel. Gorin et al. (U.S. Pat. No. 4,263,088) disclose a means of determining endpoint in a polysilicon etch using a photoconductive cell optimized to detect plasma emissions at 520 nm. By monitoring the voltage produced by this cell during the etch, it is possible to detect the change in plasma composition that occurs when the polysilicon film is completely etched. In terms of a process state function, this method can be represented by:Process State=M520nm The limitation of this approach is that a single data channel often does not generate a signal sufficiently above the system noise level to allow for reliable endpoint detection.
Other investigators have noted that endpoint detection sensitivity can be increased by using multiple data channels. Jerde et al. (U.S. Pat. No. 4,491,499) disclose measuring a narrow band of the emission spectrum while simultaneously measuring the intensity of a wider background band centered about the narrow band. In this manner the background data channels can be subtracted from the endpoint signal channels resulting in an improved endpoint signal to noise ratio.
However, the function describing the process state becomes slightly more complicated with the inclusion of multiple data channels as follows:Process State=(MSignal 1+MSignal 2+ . . . +MSignal n)−(MBackground 1+MBackground 2+ . . . +MBackground n)The fundamental limitation to the method described by Jerde et al. is that decisions regarding the selection of appropriate process data channels and the associated scaling factors have to be made based on the user's knowledge of the process. As a result, it becomes prohibitively time consuming for any one user to gain the necessary expertise to select appropriate data channels and scaling factors for all possible plasma etch applications. It is unlikely that any process state function produced through manual selection of data channels and scaling factors will be well optimized to detect a given process change. To ease this burden, several multivariate analysis techniques have been proposed.
Angell et al. (U.S. Pat. No. 5,288,367) disclose using principal component analysis (PCA) to automatically select data channels. This well known multivariate analysis technique groups correlated data channels into linear combinations that describe orthogonal components of variance in the analyzed data. By identifying the component that describes the variance associated with the process endpoint and examining the data channel constituents of that component, one can determine which channels are advantageous to monitor for detecting endpoint. The Angell et al. method process state function can be represented:Process State=f(p1M1,p2M2,p3M3, . . . , pnMn)where the pn are given by the loadings of one of the user selected principal components.
The limitation to PCA-based approaches is that the PCA algorithm attempts to describe the variance as a set of orthogonal components. PCA does not attempt to directly optimize the selection of process data channels and scaling factors for process change detection. One hopes that one of the orthogonal components completely captures the endpoint information, but PCA is not specifically directed at this outcome. The signal to noise ratio of the process state function may therefore not be sufficient for some applications.
It should be noted that while the examples presented here are specific to endpoint detection in a plasma etch process, the techniques used are representative of those employed in a wide variety of process monitoring applications. Any process whose state can be described in terms of a function of a data channel or multiple channels can be monitored with similar techniques.
Therefore, there is a need for improving the optimization of process state functions of a plasma etch process.
Nothing in the prior art provides the benefits attendant with the present invention.
Therefore, it is an object of the present invention to provide an improvement which overcomes the inadequacies of the prior art devices and which is a significant contribution to the advancement of the semiconductor processing art.
Another object of the present invention is to provide a method for detecting a process change, the method comprising placing a substrate in a chamber; exposing the substrate to a process with at least one known process change; acquiring at least one dataset during a process; and applying an evolutionary computing technique to at least one dataset to generate a process change detection algorithm.
The foregoing has outlined some of the pertinent objects of the present invention. These objects should be construed to be merely illustrative of some of the more prominent features and applications of the intended invention. Many other beneficial results can be attained by applying the disclosed invention in a different manner or modifying the invention within the scope of the disclosure. Accordingly, other objects and a fuller understanding of the invention may be had by referring to the summary of the invention and the detailed description of the preferred embodiment in addition to the scope of the invention defined by the claims taken in conjunction with the accompanying drawings.